18465/665: Advanced Probability & Statistics for Engineers
Course Description
This course will help masters and undergraduate students to obtain the background necessary for excelling in courses and careers in machine learning, artificial intelligence, and related fields. We will cover basic concepts of probability prerequisite to understanding the material typically taught in a ML course. We will also cover slightly more advanced topics including Markov Chains, hypothesis testing, and maximumlikelihood estimation. The remaining part of the semester will be devoted to introducing machine learning concepts such as supervised/unsupervised learning, model identification, clustering, expectation maximization, etc. Students should be familiar with basic calculus, linear algebra.
Number of Units: 12
Prerequisite: Basic Calculus
Course Area: Artificial Intelligence, Robotics and Control
Tentative Syllabus: Given here
Instructor and Administrative Staff
Instructor: Prof. Osman Yağan Teaching Assistants: Mansi Sood and Yichen Ruan
Office Location: HH A302
Email Address: oyagan@ece.cmu.edu
Office Hours: Mondays 2:30pm4pm
Class Schedule
Lecture: Mondays and Wednesdays 12:30 pm – 2:20 pm (EST)
Recitation: Fridays 12:30 pm – 2:20 pm (EST)
Location: WEH 5328 (PIT) and B23 118/221 (SV)
Recommended Books
Papoulis and S. U. Pillai, Probability, Random Variables, and Stochastic Processes, 4th Ed.
B. Hajek, Random Processes for Engineers, Cambridge university press, 2015
Louis L Scharf, Statistical Signal Processing, Detection, Estimation, and Time Series Analysis. 1991, 1st Ed.
Vincent Poor, An Introduction to Signal Detection and Estimation, Springer, 2nd Ed.
Larry Wasserman, All of statistics: a concise course in statistical inference, Springer.
Grading
Homeworks (7 sets)  40% 
Test 1  20% 
Test 2  20% 
Test 3  20%

